The major challenge facing health care providers in the present climate is to achieve a balance between a desire to reduce costs and the overriding need to maintain quality in patient care. The attempt to reduce costs without compromising quality centers around a two-fold effort to eliminate wasteful practices, and to concentrate resources on identifying those patients with the greatest likelihood of poor outcomes. By their very nature, both efforts require the use of accurate and comprehensive databases that can be extracted and analyzed to provide a basis for intervention. Two such areas with potential for intervention are the identification of high-risk patients that would benefit from proactive approaches, e.g., by determining their future states, and the elimination wasteful practices that increase cost without a commensurate improvement in quality, or prolong length of stay, e.g., by accurately diagnosing their current state.
The problem that confronts any such effort, however, is the lack of high-quality data that can be extracted and analyzed in any meaningful or reliable way, since most hospital databases are created in text-based or other non-structured formats. Most hospitals either resort to the use of random sampling to manually review a small proportion of patient charts, or focus on relatively easily available structured information (based, for example, on DRG or ICD-9 codes) to guide their decision-making. Any truly comprehensive changes are thus left to an imperfect process, or must await a prospective data-entry system that has the capability of acting as an adequate repository of all the differing formats in which patient data are stored. At the present time managing all these different formats presents a formidable challenge in even one hospital database, let alone in different systems.
In view of the above, there exists a need for techniques to collect patient information from a variety of sources to quickly and efficiently diagnose a current state or condition of a patient and to project the future state of the patient to help quickly identify high-risk patients, and to determine cost-effective treatments and/or therapies.